In this work, we propose introduce a variant of online stochastic gradient descent and prove
it converges to Nash equilibria and simultaneously it has sublinear regret for the class of …

Designing efficient algorithms to compute Nash equilibria poses considerable challenges in
algorithmic game theory and optimization. In this work, we employ integer programming …

We consider generalized Nash equilibrium problems (GNEPs) with non-convex strategy
spaces and non-convex cost functions. This general class of games includes the important …

We introduce an online learning algorithm in the bandit feedback model that, once adopted
by all agents of a congestion game, results in game-dynamics that converge to an $\epsilon …

We introduce a new model of congestion games that captures several extensions of the
classical congestion game introduced by Rosenthal in 1973. The idea here is to …

We consider a basic nonconvex resource allocation game, where the players' strategy
spaces are subsets of and cost functions are parameterized by some common vector and …

Congestion games allow to model competitive resource sharing in various distributed
systems. Pure Nash equilibria, that are stable outcomes of a game, could be far from being …

We consider clustering games in which the players are embedded into a network and want
to coordinate (or anti-coordinate) their strategy with their neighbors. The goal of a player is to …

The price of anarchy is the most well-known measure for quantifying the inefficiency of
equilibrium flows in traffic networks and routing games. In this work, we give unifying price of …

Path optimization is a fundamental concern across various real-world scenarios, ranging
from traffic congestion issues to efficient data routing over the internet. The Traffic …